CN107220964A - A kind of linear feature extraction is used for geology Taking stability appraisal procedure - Google Patents
A kind of linear feature extraction is used for geology Taking stability appraisal procedure Download PDFInfo
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- CN107220964A CN107220964A CN201710302861.9A CN201710302861A CN107220964A CN 107220964 A CN107220964 A CN 107220964A CN 201710302861 A CN201710302861 A CN 201710302861A CN 107220964 A CN107220964 A CN 107220964A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30184—Infrastructure
Abstract
It is used for geology Taking stability appraisal procedure the invention discloses a kind of linear feature extraction, is related to Taking stability assessment technology field.This method includes：To two phase cloud data rough registrations and essence registration；Using Kriging regression method, Dangerous Rock Body surface model is generated；Grey level quantization processing is carried out according to the Z values of every bit to the three-dimensional coordinate point set of the regular grid in Dangerous Rock Body surface model；Using edge detection operator, feature line extraction is carried out respectively to two Dangerous Rock Body surface depth images；The corresponding relation of the three-dimensional coordinate point of regular grid and pixel in Dangerous Rock Body surface depth image in Dangerous Rock Body surface model, by calculating the deflection of respective point in the three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models, stability of the Dangerous Rock Body in monitoring cycle is determined.This method can extract Dangerous Rock Body landslide surface line feature；What is obtained is three-dimensional shaped variable, can analyze rock mass internal stability from the deformation in three directions.
Description
Technical field
The present invention relates to Taking stability assessment technology field, more particularly relating to a kind of linear feature extraction is used for ground
Matter Taking stability appraisal procedure.
Background technology
In high gradient slope area, such as mountain area rock side slope, overhanging cliff etc., small-scale slope collapes are most commonly seen
Landslides, small-sized slope collapes cause Velocity of The Landslide fast, once disaster occurs, its harm is huge.
At present, the stability come down using three-dimensional laser scanning technique to various Dangerous Rock Bodies has done numerous studies, and its is main
Research Thinking is：Structural plane identification is carried out to cloud data first, then according to the structural surface information research and application cycle of extraction
The movement tendency of interior Dangerous Rock Body, these schemes can realize the deformation monitoring on Dangerous Rock Body landslide, but the result of deformation monitoring is only
It is limited to the analysis to Dangerous Rock Body local surfaces motion conditions, acquisition is one-dimensional deformation amount, and Taking stability is carried out
Analyse in depth, not only to extract principal character such as geosutures of influence Dangerous Rock Body motion etc., but also to analyze this feature edge
The misalignment of three-dimensional.
The mode that Linderbergh increases by using region carries out dough sheet separation, surface model is then set up, according to most
A young waiter in a wineshop or an inn multiplies criterion and calculates the distance between dough sheet, finally with the method analysis deformation parameter of statistical analysis, the advantage is that energy
Enough detect also to want the Dangerous Rock Body surface geometrical features under small deformation, but natural scene complicated than single-point error, do not close even
Suitable empirical function describes the geometrical property of dough sheet.Registration is carried out to the point cloud of different times using ICP algorithm, with resulting
Translational movement be used as deformation quantity.After the cloud data rough registration of multidate, its surface normal direction is almost consistent, not in view of every
Individual region point cloud relative to reference point clouds twiddle factor, although therefore the obtained deflection precision of this method is high, it can
It is low by property.
In summary, Taking stability is monitored in the prior art, there is monitoring result and be only limitted to Dangerous Rock Body part
The analysis of apparent motion situation, the problem of it analyzes not comprehensive enough.
The content of the invention
The embodiment of the present invention, which provides a kind of linear feature extraction, is used for geology Taking stability appraisal procedure, to solve
Exist in the prior art and the analysis to Dangerous Rock Body local surfaces motion conditions is only limitted to the result that Taking stability is monitored, its
The problem of analyzing not comprehensive enough.
The embodiment of the present invention, which provides a kind of linear feature extraction, is used for geology Taking stability appraisal procedure, including：
Obtain two phase cloud datas of Dangerous Rock Body surface location；
Two phase cloud datas are carried out with vegetation point data and uncorrelated noise point data removal processing；
Rough registration is carried out to two phase cloud datas by characters of ground object o'clock, and two phase cloud datas entered by ICP algorithm
Row essence registration；
Using Kriging regression method, two phase cloud datas are generated into two Dangerous Rock Body surface models respectively；
Grey level quantization is carried out according to the Z values of every bit to the three-dimensional coordinate point set of the regular grid in Dangerous Rock Body surface model
Two Dangerous Rock Body surface models are generated two Dangerous Rock Body surface depth images by processing respectively；
Using edge detection operator, feature line extraction is carried out respectively to two Dangerous Rock Body surface depth images；And according to danger
The corresponding relation of the three-dimensional coordinate point of regular grid and pixel in Dangerous Rock Body surface depth image in rock mass surface model, respectively
Obtain the three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models；
According to the three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models, by calculating two danger
In rock mass surface model in the three-dimensional coordinate point set of regular grid characteristic curve respective point deflection, determine Dangerous Rock Body monitoring
Stability in cycle.
It is preferred that Z value of the three-dimensional coordinate point set of the regular grid in the surface model to Dangerous Rock Body according to every bit
Grey level quantization processing is carried out, two Dangerous Rock Body surface models are generated into two Dangerous Rock Body surface depth images respectively；Including：
The three-dimensional coordinate point of regular grid in Dangerous Rock Body surface model is scanned for, determined in Dangerous Rock Body surface model
Regular grid three-dimensional coordinate point in greatest z value and minimum Z values；
According to greatest z value and minimum Z values, by formula (1), determine every in the regular grid in Dangerous Rock Body surface model
The grey scale pixel value of individual three-dimensional coordinate point；
X-coordinate and Y-coordinate in three-dimensional coordinate point in regular grid in Dangerous Rock Body surface model is converted into crag
The two-dimensional coordinate of body surface depth image；Wherein, the two-dimensional coordinate point of Dangerous Rock Body surface depth image and Dangerous Rock Body case depth
The pixel correspondence of image；
The grey scale pixel value and Dangerous Rock Body of each three-dimensional coordinate point in regular grid in Dangerous Rock Body surface model
The two-dimensional coordinate of surface depth image, determines Dangerous Rock Body surface depth image；
Formula (1) is as follows：
Wherein, GiFor the three-dimensional coordinate point gray value of regular grid；ZiFor the three-dimensional coordinate point height value of regular grid；Zmax
For the greatest z value in the three-dimensional coordinate point of regular grid；ZminFor the minimum Z values in the three-dimensional coordinate point of regular grid.
It is preferred that two Dangerous Rock Body surface depth images are carried out characteristic curve and carried by shown use edge detection operator respectively
Take；Including：
Dangerous Rock Body surface depth image is smoothed using Gaussian filter；
Determine amplitude and the direction of Dangerous Rock Body surface depth image；
Amplitude to Dangerous Rock Body surface depth image carries out maximizing suppression processing；
Pass through dual threshold algorithm keeps track Dangerous Rock Body surface depth image edge.
It is used for geology Taking stability appraisal procedure there is provided a kind of linear feature extraction in the embodiment of the present invention, with showing
There is technology to compare, its advantage is：This method combination digital image processing techniques extract the fracture characteristic on Dangerous Rock Body surface
Line, then compares the situation of change of two phase characteristic curve three-dimensional coordinates to analyze the degree of stability of geology Dangerous Rock Body, can extract danger
Rock slope surface line feature, acquisition is three-dimensional shaped variable, can be respectively from X, Y, the deformation analysis in tri- directions of Z
Rock mass internal stability, improves reliability；Three-dimensional coordinate for extracting characteristic curve, in X, Y, tri- Orientations of Z its
Misalignment, intuitively reflects motion conditions of the Dangerous Rock Body in monitoring cycle comprehensively.
Brief description of the drawings
Fig. 1 is that a kind of linear feature extraction provided in an embodiment of the present invention is used for geology Taking stability appraisal procedure stream
Cheng Tu.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Fig. 1 is that a kind of linear feature extraction provided in an embodiment of the present invention is used for geology Taking stability appraisal procedure stream
Cheng Tu.As shown in figure 1, this method includes：
Step S101, obtains two phase cloud datas of Dangerous Rock Body surface location.
Two phase cloud datas are carried out vegetation point data and uncorrelated noise point data removal processing by step S102.
It should be noted that Dangerous Rock Body surface vegetation growth is luxuriant, in order to ensure the precision of result, it is necessary to pick out vegetation point
Cloud and uncorrelated noise point.
It should be noted that it is after data have been gathered, to import data to Cyclone softwares to remove noise spot and vegetation point
In, remove obvious vegetation point and noise spot using the tangent plane editting function of the software.
Two phase cloud datas are carried out rough registration by step S103 by characters of ground object o'clock, and by ICP algorithm to 2 phase points
Cloud data carry out essence registration.
It should be noted that the present invention is using the method for registering being combined according to characteristic point and ICP algorithm.This is by crag
What the terrain environment residing for body was determined, high-slope dangerous rock surface can not lay permanently effective target, three-dimensional laser scanner
Distant apart from target in gathered data, return signal is weak, therefore the Target Center error extracted is larger.And this is matched somebody with somebody
Quasi- method, is first depending on selecting the obvious characteristic point on rock mass surface to complete rough registration so that many issues evidences are substantially at same ginseng
Examine in coordinate system, then application ICP registration Algorithms complete essence registration.
It should be noted that because two phase cloud datas of collection are not under same referential, so to the data of collection
Registration is carried out, registration is carried out after two issues evidence is gathered, two phase cloud datas are carried out, with punctual, to be first depending on selection
The obvious characteristic point on rock mass surface completes rough registration so that then two issues are applied according to being substantially in same reference frame
ICP registration Algorithms complete essence registration.Because Dangerous Rock Body can not lay target in the present invention, therefore carried out by choosing characters of ground object point
Rough registration and then application ICP method for registering realize essence registration.
Further, rough registration be characteristic point of the same name is chosen on two phase cloud datas will second phase point cloud coordinate and the first phase
Point cloud coordinate unification.Registering principle resolves seven parameters of rigid body translation using the point of more than three：Three rotation parameters, three
Translation parameters and a zooming parameter, i.e., each point have three conditions, and to resolve seven parameters at least needs seven conditions, profit
The transformation relation resolved with redundant observation adjustment between seven parameters, two points is as follows:
Wherein, R is spin matrix, is defined as follows：
Because the characteristic point of the same name of selection is not necessarily same point, it is understood that there may be deviation, match somebody with somebody so to carry out ICP essences
It is accurate.
Further, ICP algorithm is current widely used point cloud registration algorithm, and it is by iteration optimization matrix, each
In iterative process, to each point in target point set, concentrated in reference point and find closest approach as corresponding points, thus calculated corresponding
Coordinate transform vector, use it in target point set, obtain new target point set and enter next iterative process, until error
Convergence, finally gives outstanding transition matrix, realizes the accuracy registration of two point sets.
It should be noted that because two phase cloud datas of different times acquisition are not in same coordinate reference system, registration
Be exactly the coordinate using first phase point cloud as reference frame, by under the Coordinate Conversion of second phase point cloud to the reference frame, only
Have under two phase point clouds unification to the same coordinate system, compare just meaningful.
Two phase cloud datas, using Kriging regression method, are generated two Dangerous Rock Body surface models by step S104 respectively.
It should be noted that compared with natural neighbor interpolation, Kriging regression method will not cause surface modes because of shortage of data
Type jagged edges.
It should be noted that during actual scanning, it is general it is difficult to ensure that inside the Dangerous Rock Body of scanning just with scanning
Reference axis (X-axis or Y-axis) in instrument transversal scanning face is vertical, then need to be rotated before a cloud Surface interpolating, it is ensured that
Can be vertical with facade with a certain reference axis in transversal scanning face inside Dangerous Rock Body.
Further, the little Dangerous Rock Body of surface undulation is directed to, the cloud data of scanning is projected in the plane of scanning motion, obtained
To the point of series of discrete, straight line is fitted using these discrete points, is tried to achieve according to the slope of straight line between straight line and X-axis
Angle theta, for each data point (xi, yi, zi), rotation transformation is carried out to cloud data using following formula, obtained after rotation
Coordinate (Xi, Yi, Zi) is that is, by cloud data anglec of rotation θ about the z axis.Simultaneously as Dangerous Rock Body surface and horizontal plane are near
Like vertically, in order to meet conventional expression way, i.e. the foundation of Dangerous Rock Body digital surface model is defined by Z-direction, in addition it is also necessary to right
Coordinate system enters line translation, and Z axis is exchanged with Y-axis, so that the initial treatment of complete paired data.
Step S105, enters to the three-dimensional coordinate point set of the regular grid in Dangerous Rock Body surface model according to the Z values of every bit
Two Dangerous Rock Body surface models are generated two Dangerous Rock Body surface depth images by the processing of row grey level quantization respectively.
It is preferred that grey level quantization processing is carried out to the regular grid data in Dangerous Rock Body surface model, by two Dangerous Rock Bodies
Surface model generates two Dangerous Rock Body surface depth images respectively, specifically includes：
The three-dimensional coordinate point of regular grid in Dangerous Rock Body surface model is scanned for, determined in Dangerous Rock Body surface model
Regular grid three-dimensional coordinate point in greatest z value and minimum Z values.
According to greatest z value and minimum Z values, by formula (1), determine every in the regular grid in Dangerous Rock Body surface model
The grey scale pixel value of individual three-dimensional coordinate point.
Formula (1) is as follows：
Wherein, GiFor the three-dimensional coordinate point gray value of regular grid；ZiFor the three-dimensional coordinate point height value of regular grid；Zmax
For the greatest z value in the three-dimensional coordinate point of regular grid；ZminFor the minimum Z values in the three-dimensional coordinate point of regular grid.
X-coordinate and Y-coordinate in three-dimensional coordinate point in regular grid in Dangerous Rock Body surface model is converted into crag
The two-dimensional coordinate of body surface depth image；Wherein, the two-dimensional coordinate point of Dangerous Rock Body surface depth image and Dangerous Rock Body case depth
The pixel correspondence of image.
The grey scale pixel value and Dangerous Rock Body of each three-dimensional coordinate point in regular grid in Dangerous Rock Body surface model
The two-dimensional coordinate of surface depth image, determines Dangerous Rock Body surface depth image.
It should be noted that the regular grid data point obtained to interpolation is scanned for, greatest z value and minimum Z values are obtained,
Each elevation is quantified according to formula (1), the grey scale pixel value of each grid points is obtained；Simultaneously by regular grid data
X in three-dimensional point coordinate is converted into the two-dimensional coordinate of Dangerous Rock Body case depth image with Y-coordinate, wherein, coordinate points and pixel
Correspond, so just generate same regular grid size identical Dangerous Rock Body case depth image.
Two Dangerous Rock Body surface depth images, using edge detection operator, are carried out feature line extraction by step S106 respectively；
And according to the three-dimensional coordinate point of regular grid in Dangerous Rock Body surface model with Dangerous Rock Body surface depth image pixel it is corresponding
Relation, obtains the three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models respectively.
It is preferred that using edge detection operator, feature line extraction is carried out respectively to two Dangerous Rock Body surface depth images；Tool
Body includes：
Dangerous Rock Body surface depth image is smoothed using Gaussian filter.
Determine amplitude and the direction of Dangerous Rock Body surface depth image.
Amplitude to Dangerous Rock Body surface depth image carries out maximizing suppression processing.
Pass through dual threshold algorithm keeps track Dangerous Rock Body surface depth image edge.
It should be noted that the process of edge extracting is the discontinuity for first detecting gray scale, then by discontinuous side
Edge pixel is connected, as the feature that object is complete.Conventional edge detection operator has a Roberts operators, Sobel operators,
Prewitt operators, Laplacian operators, Canny operators.This patent uses Canny operators.Compared to other operators
The rim detection and noise immunity of Canny operators are all best.
It should be noted that the position of each data point in regular grid can use it in Grid square
Ranks p [i, j] represents that corresponding three-dimensional coordinate is p[i,j](X, Y, Z), its ranks of corresponding pixel in depth image
Number it is similarly [i, j], therefore when extracting the characteristic curve of a certain determination in depth image, can be according to this feature line in depth
The position spent in image, so as to find corresponding ranks number in Grid square, obtains the three-dimensional coordinate set of characteristic curve.
It should be noted that carrying out feature extraction to Dangerous Rock Body surface using digital image processing techniques, it will quantify to generate
Depth image carry out feature extraction, the data organizational form of regular grid and the data organizational form of depth image are identical
, a three-dimensional coordinate point of each pixel rule of correspondence grid can extract each characteristic curve according to this relation
Three-dimensional coordinate.
Step S107, according to the three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models, passes through
The deflection of respective point in the three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models is calculated, it is determined that danger
Stability of the rock mass in monitoring cycle.
It should be noted that two phase cloud datas can extract respective characteristic curve three-dimensional coordinate point by step S106
Collection, then the deflection each put can be just calculated by the coordinate between respective point, while in two characteristic curves institute a little
To deflection feature motion conditions of the whole piece characteristic curve in monitoring cycle.
It should be noted that the present invention extracts the fracture characteristic line on Dangerous Rock Body surface with reference to digital image processing techniques,
Then compare the situation of change of two phase characteristic curve three-dimensional coordinates to analyze the degree of stability of geology Dangerous Rock Body, Dangerous Rock Body can be extracted
Come down surface line feature, and acquisition is three-dimensional shaped variable, can be respectively from X, Y, the deformation analysis rock mass in tri- directions of Z
Internal stability, improves reliability；Three-dimensional coordinate for extracting characteristic curve, in X, Y, its displacement of the Orientation of Z tri-
Situation, intuitively reflects motion conditions of the Dangerous Rock Body in monitoring cycle comprehensively.
Disclosed above is only several specific embodiments of the present invention, and those skilled in the art can be to present invention progress
It is various to change with modification without departing from the spirit and scope of the present invention, if these modifications and variations of the present invention belong to the present invention
Within the scope of claim and its equivalent technologies, then the present invention is also intended to comprising including these changes and modification.
Claims (3)
1. a kind of linear feature extraction is used for geology Taking stability appraisal procedure, it is characterised in that including：
Obtain two phase cloud datas of Dangerous Rock Body surface location；
Two phase cloud datas are carried out with vegetation point data and uncorrelated noise point data removal processing；
Rough registration is carried out to two phase cloud datas by characters of ground object o'clock, and essence is carried out to two phase cloud datas by ICP algorithm
Registration；
Using Kriging regression method, two phase cloud datas are generated into two Dangerous Rock Body surface models respectively；
The three-dimensional coordinate point set of regular grid in Dangerous Rock Body surface model is carried out at grey level quantization according to the Z values of every bit
Two Dangerous Rock Body surface models are generated two Dangerous Rock Body surface depth images by reason respectively；
Using edge detection operator, feature line extraction is carried out respectively to two Dangerous Rock Body surface depth images；And according to Dangerous Rock Body
The corresponding relation of the three-dimensional coordinate point of regular grid and pixel in Dangerous Rock Body surface depth image, is obtained respectively in surface model
The three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models；
According to the three-dimensional coordinate point set of regular grid characteristic curve in two Dangerous Rock Body surface models, by calculating two Dangerous Rock Bodies
In surface model in the three-dimensional coordinate point set of regular grid characteristic curve respective point deflection, determine Dangerous Rock Body in monitoring cycle
Interior stability.
2. linear feature extraction as claimed in claim 1 is used for geology Taking stability appraisal procedure, it is characterised in that institute
State to the three-dimensional coordinate point set of the regular grid in Dangerous Rock Body surface model and to carry out grey level quantization processing according to the Z values of every bit,
Two Dangerous Rock Body surface models are generated into two Dangerous Rock Body surface depth images respectively；Including：
The three-dimensional coordinate point of regular grid in Dangerous Rock Body surface model is scanned for, the rule in Dangerous Rock Body surface model are determined
The then greatest z value in the three-dimensional coordinate point of grid and minimum Z values；
According to greatest z value and minimum Z values, by formula (1), each three in the regular grid in Dangerous Rock Body surface model are determined
The grey scale pixel value of dimension coordinate point；
X-coordinate and Y-coordinate in three-dimensional coordinate point in regular grid in Dangerous Rock Body surface model is converted into crag body surface
The two-dimensional coordinate of face depth image；Wherein, the two-dimensional coordinate point of Dangerous Rock Body surface depth image and Dangerous Rock Body surface depth image
Pixel correspondence；
The grey scale pixel value of each three-dimensional coordinate point in regular grid in Dangerous Rock Body surface model and Dangerous Rock Body surface
The two-dimensional coordinate of depth image, determines Dangerous Rock Body surface depth image；
Formula (1) is as follows：
<mrow>
<msub>
<mi>G</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>Z</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>Z</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msub>
<mi>Z</mi>
<mi>max</mi>
</msub>
<mo>-</mo>
<msub>
<mi>Z</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
</mfrac>
<mo>*</mo>
<mn>255</mn>
</mrow>
Wherein, GiFor the three-dimensional coordinate point gray value of regular grid；ZiFor the three-dimensional coordinate point height value of regular grid；ZmaxFor rule
The then greatest z value in the three-dimensional coordinate point of grid；ZminFor the minimum Z values in the three-dimensional coordinate point of regular grid.
3. linear feature extraction as claimed in claim 1 is used for geology Taking stability appraisal procedure, it is characterised in that institute
Show and use edge detection operator, feature line extraction is carried out respectively to two Dangerous Rock Body surface depth images；Including：
Dangerous Rock Body surface depth image is smoothed using Gaussian filter；
Determine amplitude and the direction of Dangerous Rock Body surface depth image；
Amplitude to Dangerous Rock Body surface depth image carries out maximizing suppression processing；
Pass through dual threshold algorithm keeps track Dangerous Rock Body surface depth image edge.
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| CN108072898A (en) * | 2018-02-11 | 2018-05-25 | 中国石油化工股份有限公司 | Geological boundry recognition methods based on panel data density estimation |
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| CN113324581A (en) * | 2021-04-26 | 2021-08-31 | 北京中关村智连安全科学研究院有限公司 | High-precision non-contact type slope dangerous rock monitoring and early warning method |
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